Topic
Stochastic programming
About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.
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TL;DR: To approximately solve the stochastic dynamic programming problem that is associated with DUE planning, a partially closed-loop receding horizon control algorithm is presented whose solution integrates prediction, estimation, and planning while also accounting for chance constraints that arise from the uncertain locations of the robot and obstacles.
Abstract: This paper presents a strategy for planning robot motions in dynamic, uncertain environments (DUEs). Successful and efficient robot operation in such environments requires reasoning about the future evolution and uncertainties of the states of the moving agents and obstacles. A novel procedure to account for future information gathering (and the quality of that information) in the planning process is presented. To approximately solve the stochastic dynamic programming problem that is associated with DUE planning, we present a partially closed-loop receding horizon control algorithm whose solution integrates prediction, estimation, and planning while also accounting for chance constraints that arise from the uncertain locations of the robot and obstacles. Simulation results in simple static and dynamic scenarios illustrate the benefit of the algorithm over classical approaches. The approach is also applied to more complicated scenarios, including agents with complex, multimodal behaviors, basic robot-agent interaction, and agent information gathering.
231 citations
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TL;DR: The basic methodology for optimal-decision models for stochastic programming models, recent developments in computation, and several practical applications are described.
Abstract: Although decisions frequently have uncertain consequences, optimal-decision models often replace those uncertainties with averages or best estimates. Limited computational capability may have motivated this practice in the past. Recent computational advances have, however, greatly expanded the range of optimal-decision models with explicit consideration of uncertainties. This article describes the basic methodology for these stochastic programming models, recent developments in computation, and several practical applications.
231 citations
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TL;DR: A generic stochastic model for the design of networks comprising both supply and return channels, organized in a closed loop system, based on the branch-and-cut procedure known as the integer L-shaped method is presented.
230 citations
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01 Jan 2000
TL;DR: This book discusses Stochastic Optimization in Asset & Liability Management, management of Quality of Service through Chance-constraints in Multimedia Networks, and management of Value-at-Risk problems with Decision Rules.
Abstract: Preface. Introduction to the Theory of Probabilistic Functions and Percentiles S. Uryasev. Pricing American Options by Simulation Using a Stochastic Mesh with Optimized Weights M. Broadie, et al. On Optimization of Unreliable Material Flow Systems Y. Ermoliev, et al. Stochastic Optimization in Asset & Liability Management: A Model for Non-Maturing Accounts K. Frauendorfer, M. Schurle. Optimization in the Space of Distribution Functions and Applications in the Bayes Analysis A.N. Golodnikov, et al. Sensitivity Analysis of Worst-Case Distribution for Probability Optimization Problems Y.S. Kan, A.I. Kibzun. On Maximum Realiability Problem in Parallel-Series Systems with Two Failure Modes V. Kirilyuk. Robust Monte Carlo Simulation for Approximate Covariance Matrices and VaR Analyses A. Kreinin, A. Levin. Structure of Optimal Stopping Strategies for American Type Options A.G. Kukush, D.S. Silvestrov. Approximation of Value-at-Risk Problems with Decision Rules R. Lepp. Managing Risk with Expected Shortfall H. Mausser, D. Rosen. On the Numerical Solution of Jointly Chance Constrained Problems J. Mayer. Management of Quality of Service through Chance-constraints in Multimedia Networks E.A. Medova, J.E. Scott. Solution of a Product Substitution Problem Using Stochastic Programming M.R. Murr, A. Prekopa. Some Remarks on the Value-at-Risk and the Conditional Value-at-risk G.Ch. Pflug. Statistical Inference of Stochastic Optimization Problems A. Shapiro.
229 citations